The search for finite-state controllers for partially observable Markov decision processes (POMDPs) is often based on approaches like gradient ascent, attractive because of their relatively low computational cost. In this paper, we illustrate a basic problem with gradient-based methods applied to POMDPs, where the sequential nature of the decision problem is at issue, and propose a new stochastic local search method as an alternative. The heuristics used in our procedure mimic the sequential reasoning inherent in optimal dynamic programming (DP) approaches. We show that our algorithm consistently finds higher quality controllers than gradient ascent, and is competitive with (and, for some problems, superior to) other state-of-the-art controller and DP-based algorithms on large-scale POMDPs.

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